Fast Moving Horizon State Estimation for Discrete-Time Systems Using Single and Multi Iteration Descent Methods

Descent algorithms based on the gradient, conjugate gradient, and Newton methods are investigated to perform optimization in moving horizon state estimation for discrete-time linear and nonlinear systems. Conditions that ensure the stability of the estimation error are established for single and mul...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on automatic control 2017-09, Vol.62 (9), p.4499-4511
Main Authors: Alessandri, Angelo, Gaggero, Mauro
Format: Article
Language:English
Subjects:
Conjugate gradient method
Cost function
Estimation error
gradient method
Gradient methods
moving horizon
Newton method
Stability analysis
State estimation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Descent algorithms based on the gradient, conjugate gradient, and Newton methods are investigated to perform optimization in moving horizon state estimation for discrete-time linear and nonlinear systems. Conditions that ensure the stability of the estimation error are established for single and multi iteration schemes with a least-squares cost function that takes into account only a batch of most recent information. Simulation results show the effectiveness of the proposed approaches also in comparison with techniques based on the Kalman filter.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2017.2660438